marketing-deploy

marketing-deploy is a skill for Claude Code, Codex from dasein108/slope-studio. It costs 79 tokens per session (741 once invoked), scanned A, original, MIT.

A workflow step that turns a selected marketing idea into a produced, published short video and links it to the journal entry used to track the idea.

In plain words
What is it for?
Use it to estimate costs, render a video from an idea, publish it to YouTube, and associate the result with its marketing record.
Why use it?
It connects content production and publishing with later measurement, while checking the allowed budget for the video.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to estimate costs, render a video from an idea, publish it to YouTube, and associate the result with its marketing record.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dasein108/slope-studio/marketing-deploy
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add dasein108/slope-studio --skill marketing-deploy
Clone the repo
git clone --depth 1 https://github.com/dasein108/slope-studio

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for marketing-deploy

README.md
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Your own site
<a href="https://agentmods.dev/skills/dasein108/slope-studio/marketing-deploy"><img src="https://agentmods.dev/badge/skills/dasein108/slope-studio/marketing-deploy/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for marketing-deploy

Your own site · 80×15
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Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 741 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.00741
Opus 5 $0.00039 $0.00370
Sonnet 5 $0.00016 $0.00148
Haiku 4.5 $0.00008 $0.00074

Measured 10d ago against content hash 10bfdcca2340, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

marketing-deploy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agent-instructions/skills/marketing-deploy/SKILL.md · 52 lines

How it starts

The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Turn one chosen bet (entry_id + idea, from the backlog pick in marketing-guru) into a published Short bound to its journal entry.

Do this

  1. Get the spend cap from the channel budget (set once via studio marketing budget --channel <name> --per-video 0.60 or --per-minute 0.40):
    CAP=$(studio marketing budget --channel <name> --for-duration <duration_s>)
    
    --for-duration returns the per-video --max-cost (flat for per-video budgets; rate × length for per-minute). If it prints (budget unset), set the budget first or pass --max-cost by hand.
  2. Produce + publish via the film-maker skill (it owns the pipeline):
    studio estimate <run_id>            # if iterating an existing run, price stage 3 first
    studio run "<idea>" --duration 60 --tier <cheap|balanced> --max-cost $CAP \
      --publish-to youtube --privacy public --channel <name>
    
    --tier cheap ≈ stills + free motion; balanced spends --max-cost on AI clips for hero scenes. --max-cost is the whole-video cap (images + clips + music): run reserves the music bed and auto-downgrades paid fal music to synth if it won't fit, so total spend stays ≤ cap. Stage 3 aborts pre-flight if the clip estimate exceeds what's left. Cheapest "still alive" recipe ≈ $0.41 (free motion-* + one ≤6s ltx hook + free local music); see docs/10-architecture/cost-model.md for the ladder.
  3. Link the run to the bet (so measure can find the video):
    studio marketing link <entry_id> <run_id> --channel <name>
    
    Pulls the YouTube id from runs/<run_id>/07_publish.json; sets status: deployed.
  4. Wait before measuring — give the Short 48–72h+ to accrue watch time.

Notes

  • link also captures production telemetry — cost, duration, animators/fx/model, and per-stage providers — from the run manifest into the bet (T3), so learn can attribute success to the effects used and you can track spend per bet.
  • Repeat backlog→deploy until ~10 videos are live to exit cold-start.

Read the full file on GitHub · 52 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 52 lines · 79 tokens per session scan A 10bfdcca2340

Subscribe to this mod's changes

marketing-deploy is a skill published in the GitHub repository dasein108/slope-studio (3 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 741 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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